Splitting an evaluation into retrieval and generation components using the same dataset provides a structured way to diagnose and improve system performance. By isolating these two stages, developers can pinpoint whether failures stem from inadequate document retrieval or poor generation quality. For example, in a question-answering system, if the retrieval component fails to find relevant documents containing the correct answer, even a perfect generator will produce incorrect results. Evaluating retrieval first ensures that the system can access the necessary information, while the generation evaluation focuses on how effectively the model synthesizes that information into a coherent response. This separation simplifies debugging and allows targeted optimizations for each component.
Using the same dataset for both evaluations ensures consistency and reduces variables that could skew results. Suppose a medical QA system is tested on a set of questions where answers exist in the provided documents. By first measuring retrieval accuracy (e.g., “Did the system find the paragraph with the answer?”), developers can identify gaps in document indexing or embedding quality. If retrieval succeeds 90% of the time but the final answer accuracy is only 60%, the bottleneck clearly lies in the generator—perhaps due to poor context handling or overfitting. Conversely, if retrieval accuracy is low, improving document preprocessing or retrieval algorithms becomes the priority. This approach avoids conflating issues and streamlines iterative improvements.
Practically, this split improves resource allocation and prioritization. For instance, in a customer support chatbot, if retrieval evaluation shows that 80% of answers are found in the knowledge base but generation accuracy is only 50%, developers might focus on fine-tuning the generator’s ability to extract key details or avoid hallucination. Alternatively, if retrieval underperforms, efforts could shift to expanding document coverage or refining search algorithms. This method also helps set realistic expectations: a system with 70% retrieval accuracy cannot achieve 90% final accuracy without addressing retrieval first. By decoupling these stages, teams can systematically address weaknesses and avoid wasting time optimizing components that aren’t the primary source of failure.
